Abstract:

Chromatin interactions have an important role in transcription regulation and therefore they can affect the function of the whole cell and the organism. To study chromatin interactions for better understanding of gene regulation, a method called Chromosome Interaction Analysis using Paired End Tags (ChIA-PET) has been developed. ChIA-PET is a high-resolution next-generation sequencing method for finding chromatin interactions which involve a protein of interest.

ChIA-PET experiments give a list of putative interactions between two chromatin sites as a result. There are several experimental laboratory steps in ChIA-PET protocol which induce high level of background noise. The aim of this thesis is to construct a statistical model for identifying the true interactions from ChIA-PET interaction count data. First, the current methods for solving this task are reviewed. Then a new method combining a Bayesian mixture model with bias removal by Poisson regression is proposed. The model parameters are estimated by using Markov chain Monte Carlo methods. The new model is implemented on Matlab and tested on real ChIA-PET data sets.

The results suggest that the proposed mixture model can quantify chromatin interactions and make good use of incorporated bias correcting. Comparison with two other methods, ChIA-PET Tool and Mango, shows that the mixture model results are partially the same as for the other two methods but there also also some interactions only found by the mixture model. Annotation analysis revealed that the mixture model results are in line with earlier research results.Kromatiini-interaktiot ovat tärkeä tekijä geenien sääntelyssä ja tätä kautta koko solun ja eliön toiminnassa. Kromatiinin muodostamat silmukat tuovat transkription käynnistävät tekijät toistensa lähelle ja näin mahdollistavat proteiinien rakentamisen.